docs(eval): Fix 1 counterfactual prompt clause — attempted and reverted

Pre-committed regression gate caught iteration-inflation on FastAPI
compound questions. K8s regression clean and pilot_005 flipped as
designed (KHR 0.67 → 1.00, answer "does not support ... service
mesh or ingress controller [source: k8s_network_policies.md]"),
but FastAPI mean tool_calls went 1.111 → 1.556 (delta +0.444 vs
+0.30 tolerance), and q024/q025 (compound multi-topic retrieval
questions) were pushed 1 → 3 tool calls, violating the "no new
cap-hits from sub-cap baseline" criterion.

All correctness metrics on both corpora held. Citation accuracy
1.00 → 1.00 across FastAPI's 27. P@5 delta −0.007. R@5 delta 0.000.
The failure is purely process-metric; q024/q025 still produce
correct answers despite the cap-hit, via the orchestrator's
"max iterations → one final complete() without tools" path.

Failure mode: the clause's trigger condition relies on subjective
LLM judgment about whether first-search content "addresses the
specific capability". The judgment is fuzzy on compound questions
with partial-topic first-search coverage. A wording refinement
might narrow misfire rate but cannot eliminate it while the
judgment itself remains fuzzy.

q023 vs q024/q025 asymmetry: q023 is a pre-existing 3-tool-call
compound that the clause did NOT fire on. q024/q025 are
structurally similar compounds that the clause DID push to the cap.
The difference is LLM-judgment selectivity, not question structure
— exactly the fragility a deterministic trigger would avoid.

Approach changed: Fix 2 (deterministic query expansion at the
SearchTool layer) deferred to a later session. Offline-testable,
no iteration-budget impact, unit-testable against
(pilot_005, q023, q024, q025) as an acceptance fixture.

Evidence retained:
- results/fastapi_preedit.json (HEAD prompt, gate baseline)
- results/fastapi_postedit.json (clause prompt, gate-failing run)
- results/k8s_preedit_pinned.json (HEAD prompt, pinned)
- results/k8s_postedit.json (clause prompt, gate-passing run)

Pre-commit discipline honored: gate fired for exactly the reason
the pre-commitment was designed — process-metric regression on
compound questions before any ship. Tolerance-relaxation post-hoc
would burn the session's strongest discipline artifact for
marginal EV. The pilot_005 finding is preserved; only the fix
approach changed.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

DECISIONS.md

@@ -782,3 +782,99 @@ evaluation path, decision criteria. Splitting into three commits
 would add noise without adding signal. None of them change the
 prompt template itself; the prompt edit is the NEXT commit and is
 the sole experimental variable the regression measures.
+
+## Fix 1 (prompt-level counterfactual clause) attempted and reverted
+
+**Outcome.** K8s regression clean on every metric (P@5, R@5, KHR,
+citation, mean tool_calls all within tolerance or unchanged); K8s
+pilot_005 flipped from refusal to documented-negative-with-citation
+as designed (KHR 0.67 → 1.00, answer contains both "service mesh"
+and "ingress controller", cites `k8s_network_policies.md`).
+**FastAPI regression failed** on the iteration-inflation tolerance:
+mean `tool_calls_made` 1.111 → 1.556 (delta +0.444, gate +0.30),
+and two retrieval questions (q024, q025) were pushed from 1 pre-edit
+tool call to 3 post-edit tool calls (hitting `max_iterations=3`
+cap), violating the pre-committed "no new cap-hits from sub-cap
+baseline" criterion.
+
+**Correctness metrics on FastAPI all held.** Citation accuracy
+stayed at 1.000 / 1.000 across all 27 questions. P@5 delta −0.007,
+R@5 delta 0.000, KHR delta +0.006. The failure is purely process
+inflation, not output regression. q024 and q025 produce identical
+P@5/R@5/KHR/citation numbers pre and post despite the cap-hit — the
+orchestrator's "max iterations hit → one final complete() without
+tools" path happened to keep answers correct, but that is
+observation, not structural protection.
+
+**Failure mode.** The clause's trigger condition — *"your first
+search returned documentation about the subject of the question
+without addressing the specific capability or feature the user is
+asking about"* — relies on subjective LLM judgment about whether
+retrieved content "addresses" a capability. The judgment is fuzzy
+on compound multi-topic questions where the first search returns
+partial-topic coverage. q024 asks about "Docker + Gunicorn workers
++ health checks + Pydantic Settings"; first search returns Docker
+content, LLM reads "documentation about the subject without
+addressing the specific capability," fires the follow-up with
+negative framing, gets nothing useful, does a third normal search
+to cover the remaining topics, hits the cap. Same pattern on q025.
+Over-firing on this class of question is an inherent fragility of
+prompt-level LLM-judged triggers; a wording refinement might
+narrow the misfire rate but cannot eliminate it as long as the
+judgment itself is fuzzy.
+
+**q023 vs q024/q025 asymmetry is a useful signal for Fix 2.** q023
+is a pre-existing 3-tool-call compound question ("custom error
+handling + CORS middleware + structured testing with dependency
+overrides"). Under the prompt clause, **q023 was unchanged** — the
+clause did not fire on it — while q024 and q025, structurally
+similar compound questions, were pushed into 3-tool-call cap-hit.
+The difference is not in question structure but in how the LLM
+interpreted the first-search return for each. That asymmetry is
+the precise reason a deterministic trigger is the right next step:
+any Fix 2 / Fix 3 candidate should be unit-testable against
+`(pilot_005, q023, q024, q025)` — the right fix must fire on
+pilot_005 and behave predictably on all three compound questions
+(either fire on all of them or none of them, but not pick them
+selectively by LLM whim).
+
+**Gate discipline honored.** The pre-committed FastAPI tolerances
+fired for exactly the reason the pre-commitment was designed:
+catching process-metric regressions before they ship. Tolerance-
+relaxation post-hoc would burn the session's strongest discipline
+artifact (pre-committed-tolerances + honored-gate) for marginal
+ship-this-approach EV. The narrow pilot_005 finding does not
+evaporate with the revert — chunk 63 (`d0806d5da91d6026`) is real,
+the negative-framing retrieval is reproducible, and Fix 2 will
+surface the documented negative the same way via a deterministic
+path.
+
+**Fix 2 deferred to a later session.** Deterministic query
+expansion at the `SearchTool` layer: when a `search_documents`
+call returns no chunk containing a direct answer string, issue a
+second internal search with negative-framing keywords and merge
+results before returning to the orchestrator. Offline-testable,
+corpus-agnostic, no LLM judgment required, no iteration-budget
+impact (the double-search happens inside a single tool call, not
+across iterations). Unit-testable against the
+`(pilot_005, q023, q024, q025)` asymmetry as an acceptance fixture.
+
+**Evidence retained.** Four result JSONs in `results/` document the
+regression measurement at the pinned `gpt-4o-mini-2024-07-18`
+snapshot in this session:
+- `fastapi_preedit.json` — 27 questions, HEAD prompt, 0.02 threshold
+- `fastapi_postedit.json` — 27 questions, clause prompt, 0.02 threshold (**gate-failing run**)
+- `k8s_preedit_pinned.json` — 6 pilots, HEAD prompt, 0.015 threshold
+- `k8s_postedit.json` — 6 pilots, clause prompt, 0.015 threshold (**gate-passing run, pilot_005 strict flip confirmed**)
+
+The previously-committed `results/k8s_preedit.json` (from `b97f00f`)
+is also a valid K8s-pinned measurement at the session-equivalent
+snapshot and remains the canonical threshold-commit evidence.
+
+**Held DECISIONS.md drafts stay held.** The counterfactual-query
+finding draft (to be updated when Fix 2 lands) and the threshold-
+calibration entry already committed at `b97f00f` are both correct
+in scope. The narrowed serving-migration deferral entry (tied to
+any external reference to the counterfactual-query fix) also stays
+deferred until Fix 2 lands, since the production/eval-harness
+prompt divergence is unchanged by this revert.

results/fastapi_postedit.json

@@ -0,0 +1,854 @@
+[
+  {
+    "question_id": "q001",
+    "question": "How do you define a path parameter in FastAPI?",
+    "category": "retrieval",
+    "difficulty": "easy",
+    "retrieval_precision": 0.4,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 0.75,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 10965.568943065591,
+    "tokens_used": {
+      "input_tokens": 1319,
+      "output_tokens": 164,
+      "estimated_cost_usd": 0.00029624999999999996
+    },
+    "answer": "In FastAPI, path parameters are defined by capturing variable segments of a URL path and passing them directly to your route handler function. They are declared using curly braces `{}` in the route path string and must have a corresponding parameter in the function signature. \n\nHere is an example of defining a path parameter:\n\n```python\nfrom fastapi import FastAPI\n\napp = FastAPI()\n\n@app.get(\"/users/{user_id}\")\nasync def read_user(user_id: int):\n    return {\"user_id\": user_id}\n```\n\nIn this example, `user_id` is a path parameter that is captured from the URL and passed to the `read_user` function as an integer [source: fastapi_path_params.md].",
+    "retrieved_sources": [
+      "fastapi_path_params.md",
+      "fastapi_request_body.md",
+      "fastapi_query_params.md",
+      "fastapi_path_params.md",
+      "fastapi_query_params.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q002",
+    "question": "What is the default page size for pagination in FastAPI and what is the maximum allowed?",
+    "category": "retrieval",
+    "difficulty": "easy",
+    "retrieval_precision": 0.6,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 1.0,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
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+    "latency_ms": 5247.50796193257,
+    "tokens_used": {
+      "input_tokens": 1430,
+      "output_tokens": 55,
+      "estimated_cost_usd": 0.0002475
+    },
+    "answer": "The default page size for pagination in FastAPI is 20 items, and the maximum allowed page size is 100 items per page [source: fastapi_pagination.md].",
+    "retrieved_sources": [
+      "fastapi_pagination.md",
+      "fastapi_pagination.md",
+      "fastapi_path_params.md",
+      "fastapi_websockets.md",
+      "fastapi_pagination.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q003",
+    "question": "How does FastAPI handle CORS and what is the default max_age for preflight caching?",
+    "category": "retrieval",
+    "difficulty": "easy",
+    "retrieval_precision": 0.8,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 1.0,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 5985.220503993332,
+    "tokens_used": {
+      "input_tokens": 1401,
+      "output_tokens": 104,
+      "estimated_cost_usd": 0.00027255
+    },
+    "answer": "FastAPI handles CORS (Cross-Origin Resource Sharing) using the `CORSMiddleware`. You can configure it by specifying parameters such as `allow_origins`, `allow_methods`, and `max_age`. The default value for `max_age` is set to 600 seconds, which determines the duration for which the results of a preflight request can be cached by the browser [source: fastapi_middleware.md].",
+    "retrieved_sources": [
+      "fastapi_middleware.md",
+      "fastapi_middleware.md",
+      "fastapi_middleware.md",
+      "fastapi_websockets.md",
+      "fastapi_middleware.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q004",
+    "question": "What algorithm and expiry time does the FastAPI security example use for JWT tokens?",
+    "category": "retrieval",
+    "difficulty": "medium",
+    "retrieval_precision": 1.0,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 1.0,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 4853.062176029198,
+    "tokens_used": {
+      "input_tokens": 1398,
+      "output_tokens": 58,
+      "estimated_cost_usd": 0.0002445
+    },
+    "answer": "The FastAPI security example uses the \"HS256\" algorithm for JWT tokens and sets the expiry time for access tokens to 30 minutes [source: fastapi_security.md].",
+    "retrieved_sources": [
+      "fastapi_security.md",
+      "fastapi_security.md",
+      "fastapi_security.md",
+      "fastapi_security.md",
+      "fastapi_security.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q005",
+    "question": "What is the recommended formula for calculating the number of Gunicorn workers for a FastAPI deployment?",
+    "category": "retrieval",
+    "difficulty": "medium",
+    "retrieval_precision": 0.6,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 0.5,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 2,
+    "latency_ms": 13312.960384995677,
+    "tokens_used": {
+      "input_tokens": 2993,
+      "output_tokens": 217,
+      "estimated_cost_usd": 0.00057915
+    },
+    "answer": "The FastAPI documentation does not provide a specific formula for calculating the number of Gunicorn workers for a deployment. However, it does mention a common configuration example where 4 workers are used with Gunicorn and Uvicorn workers:\n\n```bash\ngunicorn main:app \\\n    --workers 4 \\\n    --worker-class uvicorn.workers.UvicornWorker \\\n    --bind 0.0.0.0:8000 \\\n    --timeout 120 \\\n    --graceful-timeout 30 \\\n    --keep-alive 5 \\\n    --max-requests 1000 \\\n    --max-requests-jitter 50 \\\n    --access-logfile -\n```\n\nThis example suggests using 4 workers, but it does not specify a calculation method for determining the optimal number of workers based on system resources or expected load [source: fastapi_deployment.md].",
+    "retrieved_sources": [
+      "fastapi_deployment.md",
+      "fastapi_deployment.md",
+      "fastapi_deployment.md",
+      "fastapi_intro.md",
+      "fastapi_intro.md",
+      "fastapi_deployment.md",
+      "fastapi_deployment.md",
+      "fastapi_deployment.md",
+      "fastapi_deployment.md",
+      "fastapi_deployment.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q006",
+    "question": "How does dependency caching work in FastAPI, and how can you disable it?",
+    "category": "retrieval",
+    "difficulty": "medium",
+    "retrieval_precision": 1.0,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 1.0,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 2,
+    "latency_ms": 10240.45393196866,
+    "tokens_used": {
+      "input_tokens": 2950,
+      "output_tokens": 181,
+      "estimated_cost_usd": 0.0005510999999999999
+    },
+    "answer": "In FastAPI, dependency caching works by default such that if the same dependency is used multiple times within a single request, FastAPI caches the result and calls the dependency only once. This means that if a dependency is invoked more than once in a request, it will not be executed again; instead, the cached result will be used.\n\nTo disable this caching behavior and force a fresh call to the dependency each time it is used, you can set the `use_cache` parameter to `False` when declaring the dependency. For example:\n\n```python\nDepends(get_db, use_cache=False)\n```\n\nThis will ensure that the dependency is called anew every time it is needed within that request [source: fastapi_dependencies.md].",
+    "retrieved_sources": [
+      "fastapi_dependencies.md",
+      "fastapi_dependencies.md",
+      "fastapi_dependencies.md",
+      "fastapi_dependencies.md",
+      "fastapi_dependencies.md",
+      "fastapi_dependencies.md",
+      "fastapi_dependencies.md",
+      "fastapi_openapi.md",
+      "fastapi_dependencies.md",
+      "fastapi_security.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q007",
+    "question": "If a paginated endpoint returns 20 items per page and there are 10,000 items total, how many total pages are there? And if the page size is changed to 30, how many pages would there be?",
+    "category": "calculation",
+    "difficulty": "medium",
+    "retrieval_precision": 0.0,
+    "retrieval_recall": 0.0,
+    "keyword_hit_rate": 0.75,
+    "has_source_citation": false,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 2,
+    "latency_ms": 2763.5150860296562,
+    "tokens_used": {
+      "input_tokens": 898,
+      "output_tokens": 108,
+      "estimated_cost_usd": 0.0001995
+    },
+    "answer": "If a paginated endpoint returns 20 items per page, there would be a total of 500 pages. If the page size is changed to 30, there would be approximately 334 pages (since you cannot have a fraction of a page, you would round up).",
+    "retrieved_sources": [],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q008",
+    "question": "Does FastAPI support automatic Kubernetes deployment?",
+    "category": "out_of_scope",
+    "difficulty": "easy",
+    "retrieval_precision": 0.0,
+    "retrieval_recall": 0.0,
+    "keyword_hit_rate": 0.3333333333333333,
+    "has_source_citation": true,
+    "grounded_refusal": false,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 2,
+    "latency_ms": 7065.396980033256,
+    "tokens_used": {
+      "input_tokens": 3140,
+      "output_tokens": 84,
+      "estimated_cost_usd": 0.0005214
+    },
+    "answer": "The FastAPI documentation does not explicitly mention support for automatic Kubernetes deployment. It covers deployment using ASGI servers and Docker, but there is no specific information regarding Kubernetes deployment capabilities or limitations. Therefore, the answer is not in the FastAPI Docs documentation.",
+    "retrieved_sources": [
+      "fastapi_configuration.md",
+      "fastapi_deployment.md",
+      "fastapi_deployment.md",
+      "fastapi_deployment.md",
+      "fastapi_deployment.md",
+      "fastapi_configuration.md",
+      "fastapi_background_tasks.md",
+      "fastapi_background_tasks.md",
+      "fastapi_deployment.md",
+      "fastapi_path_params.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q009",
+    "question": "How does FastAPI integrate with Apache Kafka for event streaming?",
+    "category": "out_of_scope",
+    "difficulty": "easy",
+    "retrieval_precision": 0.0,
+    "retrieval_recall": 0.0,
+    "keyword_hit_rate": 0.3333333333333333,
+    "has_source_citation": true,
+    "grounded_refusal": false,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 2,
+    "latency_ms": 8296.601740992628,
+    "tokens_used": {
+      "input_tokens": 2659,
+      "output_tokens": 70,
+      "estimated_cost_usd": 0.00044085
+    },
+    "answer": "The FastAPI documentation does not provide specific information on how FastAPI integrates with Apache Kafka for event streaming. Therefore, I cannot answer your question based on the available documentation.",
+    "retrieved_sources": [
+      "fastapi_security.md",
+      "fastapi_intro.md",
+      "fastapi_intro.md",
+      "fastapi_websockets.md",
+      "fastapi_intro.md",
+      "fastapi_path_params.md",
+      "fastapi_pagination.md",
+      "fastapi_intro.md",
+      "fastapi_dependencies.md",
+      "fastapi_dependencies.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q010",
+    "question": "Can FastAPI generate GraphQL schemas natively?",
+    "category": "out_of_scope",
+    "difficulty": "easy",
+    "retrieval_precision": 0.0,
+    "retrieval_recall": 0.0,
+    "keyword_hit_rate": 0.3333333333333333,
+    "has_source_citation": true,
+    "grounded_refusal": false,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 2,
+    "latency_ms": 5988.564670085907,
+    "tokens_used": {
+      "input_tokens": 2857,
+      "output_tokens": 82,
+      "estimated_cost_usd": 0.00047774999999999993
+    },
+    "answer": "FastAPI does not natively support the generation of GraphQL schemas. The documentation does not mention any built-in functionality for GraphQL, indicating that this capability is not included in FastAPI's features [source: fastapi_openapi.md].",
+    "retrieved_sources": [
+      "fastapi_openapi.md",
+      "fastapi_openapi.md",
+      "fastapi_openapi.md",
+      "fastapi_response_model.md",
+      "fastapi_openapi.md",
+      "fastapi_websockets.md",
+      "fastapi_query_params.md",
+      "fastapi_background_tasks.md",
+      "fastapi_configuration.md",
+      "fastapi_response_model.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q011",
+    "question": "What is the default Swagger UI endpoint in FastAPI?",
+    "category": "retrieval",
+    "difficulty": "easy",
+    "retrieval_precision": 0.6,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 0.6666666666666666,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 3414.396518957801,
+    "tokens_used": {
+      "input_tokens": 1322,
+      "output_tokens": 41,
+      "estimated_cost_usd": 0.0002229
+    },
+    "answer": "The default Swagger UI endpoint in FastAPI is `/docs` [source: fastapi_openapi.md].",
+    "retrieved_sources": [
+      "fastapi_openapi.md",
+      "fastapi_openapi.md",
+      "fastapi_intro.md",
+      "fastapi_intro.md",
+      "fastapi_openapi.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q012",
+    "question": "How do you raise an HTTP error in a FastAPI route handler?",
+    "category": "retrieval",
+    "difficulty": "easy",
+    "retrieval_precision": 0.8,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 1.0,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 6832.770353066735,
+    "tokens_used": {
+      "input_tokens": 1385,
+      "output_tokens": 176,
+      "estimated_cost_usd": 0.00031335
+    },
+    "answer": "To raise an HTTP error in a FastAPI route handler, you can use the `HTTPException` class. Here is an example of how to do this:\n\n```python\nfrom fastapi import FastAPI, HTTPException\n\napp = FastAPI()\n\n@app.get(\"/items/{item_id}\")\nasync def read_item(item_id: str):\n    if item_id not in items:\n        raise HTTPException(status_code=404, detail=\"Item not found\")\n    return items[item_id]\n```\n\nWhen you raise `HTTPException`, it immediately terminates request processing and returns the specified status code and detail message. The `detail` parameter can be a string, list, or dictionary, and FastAPI will serialize it to JSON automatically [source: fastapi_error_handling.md].",
+    "retrieved_sources": [
+      "fastapi_error_handling.md",
+      "fastapi_error_handling.md",
+      "fastapi_error_handling.md",
+      "fastapi_dependencies.md",
+      "fastapi_error_handling.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q013",
+    "question": "How do you define a request body in FastAPI?",
+    "category": "retrieval",
+    "difficulty": "easy",
+    "retrieval_precision": 0.6,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 1.0,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 6783.527110004798,
+    "tokens_used": {
+      "input_tokens": 1455,
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+      "estimated_cost_usd": 0.00034605000000000004
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+    "answer": "In FastAPI, you define a request body using Pydantic models. A request body is typically sent as JSON in POST, PUT, or PATCH requests. Here\u2019s how you can define a request body:\n\n1. Import FastAPI and Pydantic's `BaseModel`.\n2. Create a Pydantic model that represents the structure of the request body.\n3. Use this model as a parameter in your endpoint function.\n\nHere is an example:\n\n```python\nfrom fastapi import FastAPI\nfrom pydantic import BaseModel\n\napp = FastAPI()\n\nclass Item(BaseModel):\n    name: str\n    description: str | None = None\n    price: float\n    tax: float = 0.0\n```\n\nIn this example, the `Item` model defines the expected fields in the request body, and FastAPI will automatically validate the incoming JSON against this model when a request is made [source: fastapi_request_body.md].",
+    "retrieved_sources": [
+      "fastapi_request_body.md",
+      "fastapi_request_body.md",
+      "fastapi_error_handling.md",
+      "fastapi_request_body.md",
+      "fastapi_error_handling.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q014",
+    "question": "What testing tools does FastAPI use, and what class provides the test client?",
+    "category": "retrieval",
+    "difficulty": "easy",
+    "retrieval_precision": 0.8,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 0.3333333333333333,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
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+    "latency_ms": 5719.725794973783,
+    "tokens_used": {
+      "input_tokens": 1423,
+      "output_tokens": 70,
+      "estimated_cost_usd": 0.00025544999999999995
+    },
+    "answer": "FastAPI uses the `TestClient` class for testing applications, which provides a synchronous interface for sending requests without running an actual server. For asynchronous testing, `httpx.AsyncClient` can be used instead [source: fastapi_testing.md].",
+    "retrieved_sources": [
+      "fastapi_testing.md",
+      "fastapi_testing.md",
+      "fastapi_testing.md",
+      "fastapi_openapi.md",
+      "fastapi_testing.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q015",
+    "question": "How does FastAPI manage application configuration and environment variables?",
+    "category": "retrieval",
+    "difficulty": "medium",
+    "retrieval_precision": 0.8,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 1.0,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 8019.172917003743,
+    "tokens_used": {
+      "input_tokens": 1366,
+      "output_tokens": 188,
+      "estimated_cost_usd": 0.00031769999999999997
+    },
+    "answer": "FastAPI manages application configuration and environment variables using Pydantic's `BaseSettings` class. This allows for type-safe configuration with validation, default values, and automatic reading of environment variables and `.env` files. The priority order for reading configuration values is as follows:\n\n1. Constructor arguments passed directly to `Settings()`\n2. Environment variables\n3. Variables from the `.env` file\n4. Default values defined in the model\n\nAdditionally, FastAPI supports secrets management, where if `secrets_dir` is set, Pydantic looks for files named after each field, and the contents of these files become the field values. The priority order for secrets is:\n\n1. Constructor arguments\n2. Environment variables\n3. Secret files\n4. `.env` file\n5. Default values [source: fastapi_configuration.md].",
+    "retrieved_sources": [
+      "fastapi_configuration.md",
+      "fastapi_configuration.md",
+      "fastapi_configuration.md",
+      "fastapi_configuration.md",
+      "fastapi_intro.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q016",
+    "question": "What is the minimum response size for GZip compression middleware in FastAPI, and how do you enable it?",
+    "category": "retrieval",
+    "difficulty": "medium",
+    "retrieval_precision": 1.0,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 1.0,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 5878.701361943968,
+    "tokens_used": {
+      "input_tokens": 1385,
+      "output_tokens": 118,
+      "estimated_cost_usd": 0.00027854999999999997
+    },
+    "answer": "The minimum response size for GZip compression middleware in FastAPI is set to 500 bytes by default. You can enable GZip compression by adding the `GZipMiddleware` to your FastAPI application like this:\n\n```python\nfrom fastapi.middleware.gzip import GZipMiddleware\n\napp.add_middleware(GZipMiddleware, minimum_size=500)\n```\n\nResponses smaller than the specified `minimum_size` will be sent uncompressed [source: fastapi_middleware.md].",
+    "retrieved_sources": [
+      "fastapi_middleware.md",
+      "fastapi_middleware.md",
+      "fastapi_middleware.md",
+      "fastapi_middleware.md",
+      "fastapi_middleware.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q017",
+    "question": "How do yield dependencies work in FastAPI and what is the maximum number supported per request?",
+    "category": "retrieval",
+    "difficulty": "medium",
+    "retrieval_precision": 0.8,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 0.75,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 6614.5300309872255,
+    "tokens_used": {
+      "input_tokens": 1434,
+      "output_tokens": 139,
+      "estimated_cost_usd": 0.0002985
+    },
+    "answer": "In FastAPI, yield dependencies are used to manage resource setup and cleanup. You can use `yield` in a dependency function to run setup code before the route handler executes and cleanup code after the response is sent. This is particularly useful for managing resources like database sessions or file handles. The code before `yield` runs before the handler, the yielded value is injected as the dependency, and the code after `yield` runs afterward, ensuring cleanup even if an exception occurs [source: fastapi_dependencies.md].\n\nFastAPI supports up to 32 yield dependencies per request by default [source: fastapi_dependencies.md].",
+    "retrieved_sources": [
+      "fastapi_dependencies.md",
+      "fastapi_dependencies.md",
+      "fastapi_dependencies.md",
+      "fastapi_testing.md",
+      "fastapi_dependencies.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q018",
+    "question": "What are the three documentation endpoints FastAPI exposes by default and what OpenAPI version does it use?",
+    "category": "retrieval",
+    "difficulty": "medium",
+    "retrieval_precision": 0.8,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 1.0,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 5938.157216063701,
+    "tokens_used": {
+      "input_tokens": 1363,
+      "output_tokens": 102,
+      "estimated_cost_usd": 0.00026565
+    },
+    "answer": "FastAPI exposes three documentation-related endpoints by default:\n\n1. `/docs` - Swagger UI -- interactive API explorer\n2. `/redoc` - ReDoc -- alternative documentation viewer\n3. `/openapi.json` - Raw OpenAPI schema in JSON format\n\nFastAPI uses OpenAPI version 3.1.0 for its schema generation [source: fastapi_openapi.md].",
+    "retrieved_sources": [
+      "fastapi_openapi.md",
+      "fastapi_openapi.md",
+      "fastapi_openapi.md",
+      "fastapi_intro.md",
+      "fastapi_openapi.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q019",
+    "question": "How does FastAPI handle WebSocket connections, and what must be called before sending data?",
+    "category": "retrieval",
+    "difficulty": "medium",
+    "retrieval_precision": 0.8,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 1.0,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 6348.878717981279,
+    "tokens_used": {
+      "input_tokens": 1361,
+      "output_tokens": 100,
+      "estimated_cost_usd": 0.00026415
+    },
+    "answer": "FastAPI handles WebSocket connections using Starlette's WebSocket implementation, which allows for full-duplex, bidirectional communication. To send or receive data through a WebSocket, you must first accept the connection by calling `await ws.accept()`. This method sends the HTTP 101 Switching Protocols response to the client, enabling the WebSocket communication to begin [source: fastapi_websockets.md].",
+    "retrieved_sources": [
+      "fastapi_websockets.md",
+      "fastapi_websockets.md",
+      "fastapi_websockets.md",
+      "fastapi_intro.md",
+      "fastapi_websockets.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q020",
+    "question": "For a server with 4 CPU cores, how many Gunicorn workers should be configured using the recommended formula?",
+    "category": "calculation",
+    "difficulty": "medium",
+    "retrieval_precision": 1.0,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 1.0,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": false,
+    "tool_calls_made": 1,
+    "latency_ms": 5323.60450795386,
+    "tokens_used": {
+      "input_tokens": 1393,
+      "output_tokens": 66,
+      "estimated_cost_usd": 0.00024854999999999994
+    },
+    "answer": "The recommended number of Gunicorn workers is calculated using the formula `(2 * CPU_CORES) + 1`. For a server with 4 CPU cores, this results in 9 workers [source: fastapi_deployment.md].",
+    "retrieved_sources": [
+      "fastapi_deployment.md",
+      "fastapi_deployment.md",
+      "fastapi_deployment.md",
+      "fastapi_deployment.md",
+      "fastapi_deployment.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q021",
+    "question": "If the CORS max_age is 600 seconds, how many minutes does the browser cache preflight results?",
+    "category": "calculation",
+    "difficulty": "easy",
+    "retrieval_precision": 0.0,
+    "retrieval_recall": 0.0,
+    "keyword_hit_rate": 1.0,
+    "has_source_citation": false,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 1747.607033001259,
+    "tokens_used": {
+      "input_tokens": 801,
+      "output_tokens": 40,
+      "estimated_cost_usd": 0.00014415
+    },
+    "answer": "If the CORS max_age is 600 seconds, the browser caches preflight results for 10 minutes.",
+    "retrieved_sources": [],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q022",
+    "question": "How do route ordering and dependency injection interact when building a secure FastAPI application with scoped endpoints?",
+    "category": "retrieval",
+    "difficulty": "hard",
+    "retrieval_precision": 0.4,
+    "retrieval_recall": 0.3333333333333333,
+    "keyword_hit_rate": 0.5,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 2,
+    "latency_ms": 8902.494193986058,
+    "tokens_used": {
+      "input_tokens": 2915,
+      "output_tokens": 202,
+      "estimated_cost_usd": 0.0005584499999999999
+    },
+    "answer": "FastAPI evaluates routes in the order they are defined, which is critical when you have routes that could match the same URL pattern. This means that the order of route definitions can affect which route is executed for a given request [source: fastapi_path_params.md].\n\nAdditionally, FastAPI includes a built-in dependency injection system that allows you to share logic, enforce authentication, manage database connections, and more. Dependencies are declared using `Depends()` and are resolved automatically for each request [source: fastapi_dependencies.md]. \n\nHowever, there are no specific documented limitations or interactions between route ordering and dependency injection in the context of scoped endpoints in FastAPI. Therefore, the answer to your question about how these two features interact in building a secure application is not explicitly covered in the FastAPI documentation.",
+    "retrieved_sources": [
+      "fastapi_dependencies.md",
+      "fastapi_websockets.md",
+      "fastapi_dependencies.md",
+      "fastapi_configuration.md",
+      "fastapi_intro.md",
+      "fastapi_dependencies.md",
+      "fastapi_path_params.md",
+      "fastapi_dependencies.md",
+      "fastapi_configuration.md",
+      "fastapi_middleware.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q023",
+    "question": "How would you set up a FastAPI application with custom error handling, CORS middleware, and structured testing including dependency overrides?",
+    "category": "retrieval",
+    "difficulty": "hard",
+    "retrieval_precision": 1.0,
+    "retrieval_recall": 0.3333333333333333,
+    "keyword_hit_rate": 1.0,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 3,
+    "latency_ms": 22737.167728948407,
+    "tokens_used": {
+      "input_tokens": 2828,
+      "output_tokens": 810,
+      "estimated_cost_usd": 0.0009101999999999999
+    },
+    "answer": "To set up a FastAPI application with custom error handling, CORS middleware, and structured testing including dependency overrides, you can follow these guidelines:\n\n### Custom Error Handling\nFastAPI allows you to handle errors using the `HTTPException` class and custom exception handlers. Here\u2019s how you can implement it:\n\n1. **Using `HTTPException`:**\n   You can raise an `HTTPException` in your route handlers to return error responses.\n   ```python\n   from fastapi import FastAPI, HTTPException\n\n   app = FastAPI()\n\n   @app.get(\"/items/{item_id}\")\n   async def read_item(item_id: int):\n       if item_id not in items:\n           raise HTTPException(status_code=404, detail=\"Item not found\")\n       return items[item_id]\n   ```\n\n2. **Custom Exception Handlers:**\n   You can register custom handlers for specific exceptions.\n   ```python\n   from fastapi import FastAPI, Request\n   from fastapi.responses import JSONResponse\n\n   class ItemNotFoundException(Exception):\n       def __init__(self, item_id: str):\n           self.item_id = item_id\n\n   @app.exception_handler(ItemNotFoundException)\n   async def item_not_found_exception_handler(request: Request, exc: ItemNotFoundException):\n       return JSONResponse(\n           status_code=404,\n           content={\"detail\": f\"Item {exc.item_id} not found\"},\n       )\n   ``` \n   [source: fastapi_error_handling.md]\n\n### CORS Middleware\nTo enable CORS in your FastAPI application, you can use the `CORSMiddleware` as follows:\n```python\nfrom fastapi import FastAPI\nfrom fastapi.middleware.cors import CORSMiddleware\n\napp = FastAPI()\n\napp.add_middleware(\n    CORSMiddleware,\n    allow_origins=[\"https://example.com\", \"https://app.example.com\"],\n    allow_credentials=True,\n    allow_methods=[\"GET\", \"POST\", \"PUT\", \"DELETE\"],\n    allow_headers=[\"Authorization\", \"Content-Type\"],\n    expose_headers=[\"X-Custom-Header\"],\n    max_age=600,\n)\n```\nThis configuration allows specified origins to access your API and sets up the necessary CORS headers. [source: fastapi_middleware.md]\n\n### Structured Testing with Dependency Overrides\nFor testing, you can override dependencies to inject mock services or test databases. Here\u2019s an example:\n\n1. **Define your dependency:**\n   ```python\n   from fastapi import FastAPI, Depends\n\n   app = FastAPI()\n\n   async def get_db():\n       db = ProductionDatabase()\n       try:\n           yield db\n       finally:\n           db.close()\n\n   @app.get(\"/items/\")\n   async def read_items(db=Depends(get_db)):\n       return db.query_all_items()\n   ```\n\n2. **Override the dependency in your tests:**\n   ```python\n   from fastapi.testclient import TestClient\n\n   def get_test_db():\n       db = TestDatabase()\n       try:\n           yield db\n       finally:\n           db.close()\n\n   app.dependency_overrides[get_db] = get_test_db\n\n   client = TestClient(app)\n\n   def test_read_items():\n       response = client.get(\"/items/\")\n       assert response.status_code == 200\n   ```\n   After your tests, you should clear the overrides to prevent them from leaking into other tests:\n   ```python\n   app.dependency_overrides.clear()\n   ```\n   [source: fastapi_testing.md] \n\nBy following these steps, you can effectively set up a FastAPI application with custom error handling, CORS middleware, and structured testing with dependency overrides.",
+    "retrieved_sources": [
+      "fastapi_error_handling.md",
+      "fastapi_error_handling.md",
+      "fastapi_error_handling.md",
+      "fastapi_error_handling.md",
+      "fastapi_error_handling.md",
+      "fastapi_middleware.md",
+      "fastapi_middleware.md",
+      "fastapi_middleware.md",
+      "fastapi_middleware.md",
+      "fastapi_middleware.md",
+      "fastapi_testing.md",
+      "fastapi_testing.md",
+      "fastapi_configuration.md",
+      "fastapi_testing.md",
+      "fastapi_dependencies.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q024",
+    "question": "Explain how to deploy a FastAPI app with Docker using Gunicorn workers, health checks, and environment-based configuration via Pydantic Settings.",
+    "category": "retrieval",
+    "difficulty": "hard",
+    "retrieval_precision": 0.8,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 1.0,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 3,
+    "latency_ms": 26342.817361000925,
+    "tokens_used": {
+      "input_tokens": 3788,
+      "output_tokens": 751,
+      "estimated_cost_usd": 0.0010187999999999998
+    },
+    "answer": "To deploy a FastAPI app with Docker using Gunicorn workers, health checks, and environment-based configuration via Pydantic Settings, follow these steps:\n\n### 1. Dockerfile Setup\nCreate a `Dockerfile` for your FastAPI application. Here\u2019s an example:\n\n```dockerfile\nFROM python:3.9\n\n# Set the working directory\nWORKDIR /app\n\n# Copy the requirements file and install dependencies\nCOPY requirements.txt .\nRUN pip install --no-cache-dir -r requirements.txt\n\n# Copy the application code\nCOPY . .\n\n# Expose the port the app runs on\nEXPOSE 8000\n\n# Health check configuration\nHEALTHCHECK --interval=30s --timeout=10s --retries=3 --start-period=10s \\\n    CMD curl -f http://localhost:8000/health || exit 1\n\n# Command to run the application\nCMD [\"gunicorn\", \"main:app\", \"--workers\", \"4\", \"--worker-class\", \"uvicorn.workers.UvicornWorker\", \"--bind\", \"0.0.0.0:8000\", \"--timeout\", \"120\", \"--graceful-timeout\", \"30\", \"--keep-alive\", \"5\", \"--max-requests\", \"1000\", \"--max-requests-jitter\", \"50\", \"--access-logfile\", \"-\"]\n```\nThis configuration uses Gunicorn with Uvicorn workers to serve the FastAPI application, allowing for multiple worker processes [source: fastapi_deployment.md].\n\n### 2. Health Check Endpoint\nAdd a health check endpoint in your FastAPI application:\n\n```python\nfrom fastapi import FastAPI\n\napp = FastAPI()\n\n@app.get(\"/health\", status_code=200)\nasync def health_check():\n    return {\"status\": \"healthy\"}\n```\nThis endpoint will be used by Docker to check the health of the application [source: fastapi_deployment.md].\n\n### 3. Environment-Based Configuration with Pydantic Settings\nUse Pydantic's `BaseSettings` to manage your application configuration through environment variables. First, install the necessary package:\n\n```bash\npip install pydantic-settings\n```\n\nThen, define your settings as follows:\n\n```python\nfrom pydantic_settings import BaseSettings, SettingsConfigDict\n\nclass Settings(BaseSettings):\n    model_config = SettingsConfigDict(\n        env_file=\".env\",\n        env_file_encoding=\"utf-8\",\n        case_sensitive=False,\n    )\n    \n    database_url: str\n    api_key: str\n```\nCreate a `.env` file in your project root to store your environment variables:\n\n```\nDATABASE_URL=your_database_url\nAPI_KEY=your_api_key\n```\nPydantic Settings will read values from the environment variables, `.env` file, and provide type-safe configuration with validation [source: fastapi_configuration.md].\n\n### 4. Build and Run the Docker Container\nFinally, build and run your Docker container:\n\n```bash\ndocker build -t myfastapiapp .\ndocker run -d -p 8000:8000 myfastapiapp\n```\n\nThis setup will allow you to deploy your FastAPI application with Docker, utilizing Gunicorn for handling multiple requests, health checks for monitoring, and Pydantic for configuration management.",
+    "retrieved_sources": [
+      "fastapi_deployment.md",
+      "fastapi_configuration.md",
+      "fastapi_configuration.md",
+      "fastapi_request_body.md",
+      "fastapi_deployment.md",
+      "fastapi_deployment.md",
+      "fastapi_deployment.md",
+      "fastapi_intro.md",
+      "fastapi_deployment.md",
+      "fastapi_intro.md",
+      "fastapi_configuration.md",
+      "fastapi_intro.md",
+      "fastapi_configuration.md",
+      "fastapi_configuration.md",
+      "fastapi_configuration.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q025",
+    "question": "How would you build a paginated API with cursor-based navigation, response model validation, and background task processing for analytics logging?",
+    "category": "retrieval",
+    "difficulty": "hard",
+    "retrieval_precision": 1.0,
+    "retrieval_recall": 0.6666666666666666,
+    "keyword_hit_rate": 1.0,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 3,
+    "latency_ms": 26899.34883604292,
+    "tokens_used": {
+      "input_tokens": 3893,
+      "output_tokens": 663,
+      "estimated_cost_usd": 0.00098175
+    },
+    "answer": "To build a paginated API with cursor-based navigation, response model validation, and background task processing for analytics logging in FastAPI, you can follow these guidelines:\n\n1. **Cursor-Based Pagination**: Use an opaque token (cursor) that points to the last item in the previous page. This method avoids performance degradation associated with large offsets. Here\u2019s a basic example of how to implement cursor-based pagination:\n\n   ```python\n   from fastapi import FastAPI, Query\n   from typing import List\n\n   app = FastAPI()\n\n   @app.get(\"/items/\", response_model=PaginatedResponse[Item])\n   async def list_items(cursor: str = None, limit: int = Query(default=20, ge=1, le=100)):\n       # Logic to retrieve items based on cursor\n       return PaginatedResponse(items=items, has_more=has_more)\n   ```\n\n   Cursor-based pagination is recommended for large datasets or real-time feeds where items may be inserted or deleted between requests [source: fastapi_pagination.md].\n\n2. **Response Model Validation**: Use the `response_model` parameter in your route decorators to declare the shape of the data your endpoint returns. FastAPI will validate, serialize, and document the response, filtering out any fields not defined in the model. Here\u2019s an example:\n\n   ```python\n   from pydantic import BaseModel\n\n   class Item(BaseModel):\n       id: int\n       name: str\n\n   @app.get(\"/items/\", response_model=List[Item])\n   async def get_items():\n       return [{\"id\": 1, \"name\": \"Item 1\"}, {\"id\": 2, \"name\": \"Item 2\"}]\n   ```\n\n   This ensures that the response adheres to the defined model, providing automatic validation and serialization [source: fastapi_response_model.md].\n\n3. **Background Task Processing**: Use FastAPI's background tasks to log analytics after the response has been sent. You can define a background task function and add it to your route handler. Here\u2019s an example:\n\n   ```python\n   from fastapi import FastAPI, BackgroundTasks\n\n   app = FastAPI()\n\n   def log_analytics(item_id: int):\n       with open(\"analytics.log\", \"a\") as f:\n           f.write(f\"Item {item_id} accessed\\n\")\n\n   @app.get(\"/items/{item_id}\")\n   async def read_item(item_id: int, background_tasks: BackgroundTasks):\n       background_tasks.add_task(log_analytics, item_id)\n       return {\"item_id\": item_id}\n   ```\n\n   This allows the logging to occur in the background, ensuring that the client receives a response immediately [source: fastapi_background_tasks.md].\n\nBy combining these elements, you can create a robust API that efficiently handles large datasets, validates responses, and processes background tasks for logging or analytics.",
+    "retrieved_sources": [
+      "fastapi_pagination.md",
+      "fastapi_pagination.md",
+      "fastapi_background_tasks.md",
+      "fastapi_pagination.md",
+      "fastapi_pagination.md",
+      "fastapi_response_model.md",
+      "fastapi_intro.md",
+      "fastapi_error_handling.md",
+      "fastapi_request_body.md",
+      "fastapi_response_model.md",
+      "fastapi_background_tasks.md",
+      "fastapi_background_tasks.md",
+      "fastapi_intro.md",
+      "fastapi_background_tasks.md",
+      "fastapi_background_tasks.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q026",
+    "question": "Does FastAPI have built-in support for database migrations like Alembic?",
+    "category": "out_of_scope",
+    "difficulty": "easy",
+    "retrieval_precision": 0.0,
+    "retrieval_recall": 0.0,
+    "keyword_hit_rate": 0.3333333333333333,
+    "has_source_citation": false,
+    "grounded_refusal": false,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 2,
+    "latency_ms": 7060.65729400143,
+    "tokens_used": {
+      "input_tokens": 1241,
+      "output_tokens": 47,
+      "estimated_cost_usd": 0.00021434999999999998
+    },
+    "answer": "The answer is not in the FastAPI Docs documentation.",
+    "retrieved_sources": [],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q027",
+    "question": "How does FastAPI handle automatic load balancing across multiple servers?",
+    "category": "out_of_scope",
+    "difficulty": "easy",
+    "retrieval_precision": 0.0,
+    "retrieval_recall": 0.0,
+    "keyword_hit_rate": 0.3333333333333333,
+    "has_source_citation": true,
+    "grounded_refusal": false,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 2,
+    "latency_ms": 9740.152830956504,
+    "tokens_used": {
+      "input_tokens": 1822,
+      "output_tokens": 94,
+      "estimated_cost_usd": 0.0003297
+    },
+    "answer": "The FastAPI documentation does not provide information on how FastAPI handles automatic load balancing across multiple servers. Additionally, there are no specific limitations or capabilities mentioned regarding load balancing in the context of FastAPI. Therefore, I cannot provide an answer to your question based on the FastAPI Docs documentation.",
+    "retrieved_sources": [
+      "fastapi_background_tasks.md",
+      "fastapi_path_params.md",
+      "fastapi_background_tasks.md",
+      "fastapi_background_tasks.md",
+      "fastapi_background_tasks.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  }
+]

results/fastapi_preedit.json

@@ -0,0 +1,798 @@
+[
+  {
+    "question_id": "q001",
+    "question": "How do you define a path parameter in FastAPI?",
+    "category": "retrieval",
+    "difficulty": "easy",
+    "retrieval_precision": 0.4,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 0.75,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 11451.030120020732,
+    "tokens_used": {
+      "input_tokens": 1025,
+      "output_tokens": 161,
+      "estimated_cost_usd": 0.00025035
+    },
+    "answer": "In FastAPI, path parameters are defined by capturing variable segments of a URL path and passing them directly to your route handler function. They are declared using curly braces `{}` in the route path string and must have a corresponding parameter in the function signature. \n\nHere is a basic example:\n\n```python\nfrom fastapi import FastAPI\n\napp = FastAPI()\n\n@app.get(\"/users/{user_id}\")\nasync def read_user(user_id: int):\n    return {\"user_id\": user_id}\n```\n\nIn this example, `user_id` is a path parameter that will be captured from the URL when a request is made to `/users/{user_id}` [source: fastapi_path_params.md].",
+    "retrieved_sources": [
+      "fastapi_path_params.md",
+      "fastapi_request_body.md",
+      "fastapi_query_params.md",
+      "fastapi_path_params.md",
+      "fastapi_query_params.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q002",
+    "question": "What is the default page size for pagination in FastAPI and what is the maximum allowed?",
+    "category": "retrieval",
+    "difficulty": "easy",
+    "retrieval_precision": 0.6,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 1.0,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 5951.742989942431,
+    "tokens_used": {
+      "input_tokens": 1136,
+      "output_tokens": 55,
+      "estimated_cost_usd": 0.00020339999999999998
+    },
+    "answer": "The default page size for pagination in FastAPI is 20 items, and the maximum allowed page size is 100 items per page [source: fastapi_pagination.md].",
+    "retrieved_sources": [
+      "fastapi_pagination.md",
+      "fastapi_pagination.md",
+      "fastapi_path_params.md",
+      "fastapi_websockets.md",
+      "fastapi_pagination.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q003",
+    "question": "How does FastAPI handle CORS and what is the default max_age for preflight caching?",
+    "category": "retrieval",
+    "difficulty": "easy",
+    "retrieval_precision": 1.0,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 1.0,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 3878.671157057397,
+    "tokens_used": {
+      "input_tokens": 1075,
+      "output_tokens": 62,
+      "estimated_cost_usd": 0.00019844999999999997
+    },
+    "answer": "FastAPI handles CORS (Cross-Origin Resource Sharing) using the `CORSMiddleware`. The default `max_age` for preflight caching is set to 600 seconds [source: fastapi_middleware.md].",
+    "retrieved_sources": [
+      "fastapi_middleware.md",
+      "fastapi_middleware.md",
+      "fastapi_middleware.md",
+      "fastapi_middleware.md",
+      "fastapi_middleware.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q004",
+    "question": "What algorithm and expiry time does the FastAPI security example use for JWT tokens?",
+    "category": "retrieval",
+    "difficulty": "medium",
+    "retrieval_precision": 1.0,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 1.0,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 4879.487166996114,
+    "tokens_used": {
+      "input_tokens": 1096,
+      "output_tokens": 57,
+      "estimated_cost_usd": 0.0001986
+    },
+    "answer": "The FastAPI security example uses the following settings for JWT tokens:\n\n- **Algorithm**: HS256\n- **Expiry Time**: 30 minutes [source: fastapi_security.md].",
+    "retrieved_sources": [
+      "fastapi_security.md",
+      "fastapi_security.md",
+      "fastapi_security.md",
+      "fastapi_security.md",
+      "fastapi_security.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q005",
+    "question": "What is the recommended formula for calculating the number of Gunicorn workers for a FastAPI deployment?",
+    "category": "retrieval",
+    "difficulty": "medium",
+    "retrieval_precision": 0.6,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 0.5,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 7699.791015009396,
+    "tokens_used": {
+      "input_tokens": 1122,
+      "output_tokens": 176,
+      "estimated_cost_usd": 0.0002739
+    },
+    "answer": "The recommended formula for calculating the number of Gunicorn workers for a FastAPI deployment is to use the following command:\n\n```bash\ngunicorn main:app \\\n    --workers 4 \\\n    --worker-class uvicorn.workers.UvicornWorker \\\n    --bind 0.0.0.0:8000 \\\n    --timeout 120 \\\n    --graceful-timeout 30 \\\n    --keep-alive 5 \\\n    --max-requests 1000 \\\n    --max-requests-jitter 50 \\\n    --access-logfile -\n```\n\nIn this example, `--workers 4` specifies the number of worker processes to be 4, which is a common starting point for production deployments [source: fastapi_deployment.md].",
+    "retrieved_sources": [
+      "fastapi_deployment.md",
+      "fastapi_deployment.md",
+      "fastapi_deployment.md",
+      "fastapi_intro.md",
+      "fastapi_intro.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q006",
+    "question": "How does dependency caching work in FastAPI, and how can you disable it?",
+    "category": "retrieval",
+    "difficulty": "medium",
+    "retrieval_precision": 1.0,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 1.0,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 8104.563120054081,
+    "tokens_used": {
+      "input_tokens": 1074,
+      "output_tokens": 175,
+      "estimated_cost_usd": 0.0002661
+    },
+    "answer": "In FastAPI, dependency caching works by caching the result of a dependency if it is used multiple times within a single request. This means that if the same dependency is called more than once (for example, both a route and a sub-dependency use `Depends(get_db)`), FastAPI will only call the dependency once and reuse the result for subsequent calls within that request.\n\nTo disable this caching behavior and force a fresh call to the dependency each time it is used, you can set the `use_cache` parameter to `False` when declaring the dependency. For example:\n\n```python\nDepends(get_db, use_cache=False)\n```\n\nThis will ensure that the dependency is executed anew each time it is needed, rather than using a cached result [source: fastapi_dependencies.md].",
+    "retrieved_sources": [
+      "fastapi_dependencies.md",
+      "fastapi_dependencies.md",
+      "fastapi_dependencies.md",
+      "fastapi_dependencies.md",
+      "fastapi_dependencies.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q007",
+    "question": "If a paginated endpoint returns 20 items per page and there are 10,000 items total, how many total pages are there? And if the page size is changed to 30, how many pages would there be?",
+    "category": "calculation",
+    "difficulty": "medium",
+    "retrieval_precision": 0.0,
+    "retrieval_recall": 0.0,
+    "keyword_hit_rate": 0.75,
+    "has_source_citation": false,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 2,
+    "latency_ms": 3421.634000958875,
+    "tokens_used": {
+      "input_tokens": 604,
+      "output_tokens": 110,
+      "estimated_cost_usd": 0.0001566
+    },
+    "answer": "If a paginated endpoint returns 20 items per page, there would be a total of 500 pages. If the page size is changed to 30, there would be approximately 333 pages (specifically, 333.33, which means 334 pages when rounded up).",
+    "retrieved_sources": [],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q008",
+    "question": "Does FastAPI support automatic Kubernetes deployment?",
+    "category": "out_of_scope",
+    "difficulty": "easy",
+    "retrieval_precision": 0.0,
+    "retrieval_recall": 0.0,
+    "keyword_hit_rate": 0.3333333333333333,
+    "has_source_citation": true,
+    "grounded_refusal": false,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 5193.1978890206665,
+    "tokens_used": {
+      "input_tokens": 1148,
+      "output_tokens": 46,
+      "estimated_cost_usd": 0.00019979999999999998
+    },
+    "answer": "The FastAPI documentation does not explicitly mention support for automatic Kubernetes deployment. Therefore, I cannot provide an answer based on the FastAPI Docs documentation.",
+    "retrieved_sources": [
+      "fastapi_configuration.md",
+      "fastapi_deployment.md",
+      "fastapi_deployment.md",
+      "fastapi_deployment.md",
+      "fastapi_deployment.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q009",
+    "question": "How does FastAPI integrate with Apache Kafka for event streaming?",
+    "category": "out_of_scope",
+    "difficulty": "easy",
+    "retrieval_precision": 0.0,
+    "retrieval_recall": 0.0,
+    "keyword_hit_rate": 0.6666666666666666,
+    "has_source_citation": true,
+    "grounded_refusal": false,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 6947.794683976099,
+    "tokens_used": {
+      "input_tokens": 932,
+      "output_tokens": 53,
+      "estimated_cost_usd": 0.0001716
+    },
+    "answer": "The FastAPI Docs documentation does not contain any information regarding the integration of FastAPI with Apache Kafka for event streaming. Therefore, I cannot provide an answer to your question.",
+    "retrieved_sources": [
+      "fastapi_security.md",
+      "fastapi_intro.md",
+      "fastapi_intro.md",
+      "fastapi_websockets.md",
+      "fastapi_intro.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q010",
+    "question": "Can FastAPI generate GraphQL schemas natively?",
+    "category": "out_of_scope",
+    "difficulty": "easy",
+    "retrieval_precision": 0.0,
+    "retrieval_recall": 0.0,
+    "keyword_hit_rate": 0.3333333333333333,
+    "has_source_citation": true,
+    "grounded_refusal": false,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 4775.999667006545,
+    "tokens_used": {
+      "input_tokens": 1003,
+      "output_tokens": 52,
+      "estimated_cost_usd": 0.00018165
+    },
+    "answer": "The FastAPI documentation does not mention the capability to generate GraphQL schemas natively. Therefore, I cannot provide an answer to your question based on the FastAPI Docs documentation.",
+    "retrieved_sources": [
+      "fastapi_openapi.md",
+      "fastapi_openapi.md",
+      "fastapi_openapi.md",
+      "fastapi_response_model.md",
+      "fastapi_openapi.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q011",
+    "question": "What is the default Swagger UI endpoint in FastAPI?",
+    "category": "retrieval",
+    "difficulty": "easy",
+    "retrieval_precision": 0.6,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 0.6666666666666666,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 4869.098415947519,
+    "tokens_used": {
+      "input_tokens": 1027,
+      "output_tokens": 39,
+      "estimated_cost_usd": 0.00017744999999999998
+    },
+    "answer": "The default Swagger UI endpoint in FastAPI is `/docs` [source: fastapi_openapi.md].",
+    "retrieved_sources": [
+      "fastapi_openapi.md",
+      "fastapi_openapi.md",
+      "fastapi_openapi.md",
+      "fastapi_intro.md",
+      "fastapi_intro.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q012",
+    "question": "How do you raise an HTTP error in a FastAPI route handler?",
+    "category": "retrieval",
+    "difficulty": "easy",
+    "retrieval_precision": 0.8,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 1.0,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 6989.395670942031,
+    "tokens_used": {
+      "input_tokens": 1091,
+      "output_tokens": 176,
+      "estimated_cost_usd": 0.00026924999999999996
+    },
+    "answer": "To raise an HTTP error in a FastAPI route handler, you can use the `HTTPException` class. Here is an example of how to do this:\n\n```python\nfrom fastapi import FastAPI, HTTPException\n\napp = FastAPI()\n\n@app.get(\"/items/{item_id}\")\nasync def read_item(item_id: str):\n    if item_id not in items:\n        raise HTTPException(status_code=404, detail=\"Item not found\")\n    return items[item_id]\n```\n\nWhen you raise `HTTPException`, it immediately terminates request processing and returns the specified status code and detail message. The `detail` parameter can be a string, list, or dictionary, and FastAPI will serialize it to JSON automatically [source: fastapi_error_handling.md].",
+    "retrieved_sources": [
+      "fastapi_error_handling.md",
+      "fastapi_error_handling.md",
+      "fastapi_error_handling.md",
+      "fastapi_dependencies.md",
+      "fastapi_error_handling.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q013",
+    "question": "How do you define a request body in FastAPI?",
+    "category": "retrieval",
+    "difficulty": "easy",
+    "retrieval_precision": 0.4,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 1.0,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 8555.312847020105,
+    "tokens_used": {
+      "input_tokens": 1024,
+      "output_tokens": 207,
+      "estimated_cost_usd": 0.0002778
+    },
+    "answer": "In FastAPI, a request body is defined using Pydantic models. A request body typically contains data sent by the client to your API, often in JSON format for POST, PUT, or PATCH requests. Here\u2019s how you can define a request body:\n\n1. Import FastAPI and Pydantic's BaseModel.\n2. Create a Pydantic model that represents the structure of the request body.\n\nHere is an example:\n\n```python\nfrom fastapi import FastAPI\nfrom pydantic import BaseModel\n\napp = FastAPI()\n\nclass Item(BaseModel):\n    name: str\n    description: str | None = None\n    price: float\n    tax: float = 0.0\n```\n\nIn this example, the `Item` class is a Pydantic model that defines the expected fields in the request body, including their types and optional values [source: fastapi_request_body.md].",
+    "retrieved_sources": [
+      "fastapi_request_body.md",
+      "fastapi_request_body.md",
+      "fastapi_error_handling.md",
+      "fastapi_error_handling.md",
+      "fastapi_error_handling.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q014",
+    "question": "What testing tools does FastAPI use, and what class provides the test client?",
+    "category": "retrieval",
+    "difficulty": "easy",
+    "retrieval_precision": 0.8,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 0.3333333333333333,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 7168.439702014439,
+    "tokens_used": {
+      "input_tokens": 1129,
+      "output_tokens": 72,
+      "estimated_cost_usd": 0.00021255
+    },
+    "answer": "FastAPI applications are tested using the `TestClient` class, which provides a synchronous interface for sending requests to your application without running an actual server. For asynchronous testing, you can use `httpx.AsyncClient` [source: fastapi_testing.md].",
+    "retrieved_sources": [
+      "fastapi_testing.md",
+      "fastapi_testing.md",
+      "fastapi_testing.md",
+      "fastapi_openapi.md",
+      "fastapi_testing.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q015",
+    "question": "How does FastAPI manage application configuration and environment variables?",
+    "category": "retrieval",
+    "difficulty": "medium",
+    "retrieval_precision": 0.8,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 1.0,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 9010.0379610667,
+    "tokens_used": {
+      "input_tokens": 1072,
+      "output_tokens": 202,
+      "estimated_cost_usd": 0.000282
+    },
+    "answer": "FastAPI manages application configuration and environment variables using Pydantic's `BaseSettings` class. This allows for type-safe configuration with validation, default values, and automatic reading of environment variables and `.env` files. The priority order for reading configuration values is as follows:\n\n1. Constructor arguments passed directly to `Settings()`\n2. Environment variables\n3. Variables from the `.env` file\n4. Default values defined in the model\n\nAdditionally, FastAPI supports secrets management, where if `secrets_dir` is set, Pydantic looks for files named after each field, and the contents of these files become the field values. The priority order for secrets is:\n\n1. Constructor arguments\n2. Environment variables\n3. Secret files\n4. `.env` file\n5. Default values\n\nThis setup allows for flexible and secure management of application settings and secrets [source: fastapi_configuration.md].",
+    "retrieved_sources": [
+      "fastapi_configuration.md",
+      "fastapi_configuration.md",
+      "fastapi_configuration.md",
+      "fastapi_configuration.md",
+      "fastapi_intro.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q016",
+    "question": "What is the minimum response size for GZip compression middleware in FastAPI, and how do you enable it?",
+    "category": "retrieval",
+    "difficulty": "medium",
+    "retrieval_precision": 1.0,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 1.0,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 7100.143527961336,
+    "tokens_used": {
+      "input_tokens": 1091,
+      "output_tokens": 110,
+      "estimated_cost_usd": 0.00022965
+    },
+    "answer": "The minimum response size for GZip compression middleware in FastAPI is set to 500 bytes by default. You can enable GZip compression by adding the middleware to your FastAPI application like this:\n\n```python\nfrom fastapi.middleware.gzip import GZipMiddleware\n\napp.add_middleware(GZipMiddleware, minimum_size=500)\n```\n\nResponses smaller than this threshold will be sent uncompressed [source: fastapi_middleware.md].",
+    "retrieved_sources": [
+      "fastapi_middleware.md",
+      "fastapi_middleware.md",
+      "fastapi_middleware.md",
+      "fastapi_middleware.md",
+      "fastapi_middleware.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q017",
+    "question": "How do yield dependencies work in FastAPI and what is the maximum number supported per request?",
+    "category": "retrieval",
+    "difficulty": "medium",
+    "retrieval_precision": 1.0,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 0.75,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 6820.307894027792,
+    "tokens_used": {
+      "input_tokens": 1053,
+      "output_tokens": 134,
+      "estimated_cost_usd": 0.00023834999999999997
+    },
+    "answer": "In FastAPI, yield dependencies allow you to run setup code before the route handler executes and cleanup code after the response is sent. This is particularly useful for managing resources like database sessions. You can define a yield dependency using the `yield` statement in a function, which will execute the code before the `yield` when the dependency is called, and the code after the `yield` will run after the response is sent [source: fastapi_dependencies.md].\n\nFastAPI supports up to 32 yield dependencies per request by default [source: fastapi_dependencies.md].",
+    "retrieved_sources": [
+      "fastapi_dependencies.md",
+      "fastapi_dependencies.md",
+      "fastapi_dependencies.md",
+      "fastapi_dependencies.md",
+      "fastapi_dependencies.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q018",
+    "question": "What are the three documentation endpoints FastAPI exposes by default and what OpenAPI version does it use?",
+    "category": "retrieval",
+    "difficulty": "medium",
+    "retrieval_precision": 0.8,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 1.0,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 6209.570198901929,
+    "tokens_used": {
+      "input_tokens": 1070,
+      "output_tokens": 105,
+      "estimated_cost_usd": 0.0002235
+    },
+    "answer": "FastAPI exposes three documentation-related endpoints by default:\n\n1. `/docs` - Swagger UI, an interactive API explorer.\n2. `/redoc` - ReDoc, an alternative documentation viewer.\n3. `/openapi.json` - Raw OpenAPI schema in JSON format.\n\nFastAPI uses OpenAPI version 3.1.0 for its schema generation [source: fastapi_openapi.md].",
+    "retrieved_sources": [
+      "fastapi_openapi.md",
+      "fastapi_openapi.md",
+      "fastapi_intro.md",
+      "fastapi_openapi.md",
+      "fastapi_openapi.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q019",
+    "question": "How does FastAPI handle WebSocket connections, and what must be called before sending data?",
+    "category": "retrieval",
+    "difficulty": "medium",
+    "retrieval_precision": 0.8,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 0.75,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 6920.212193974294,
+    "tokens_used": {
+      "input_tokens": 1067,
+      "output_tokens": 109,
+      "estimated_cost_usd": 0.00022544999999999998
+    },
+    "answer": "FastAPI handles WebSocket connections using Starlette's WebSocket implementation, which allows for full-duplex, bidirectional communication. To set up a WebSocket endpoint, you use the `@app.websocket()` decorator. \n\nBefore sending or receiving any data, you must call `await ws.accept()`. This method sends the HTTP 101 Switching Protocols response to the client, establishing the WebSocket connection [source: fastapi_websockets.md].",
+    "retrieved_sources": [
+      "fastapi_websockets.md",
+      "fastapi_websockets.md",
+      "fastapi_websockets.md",
+      "fastapi_intro.md",
+      "fastapi_websockets.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q020",
+    "question": "For a server with 4 CPU cores, how many Gunicorn workers should be configured using the recommended formula?",
+    "category": "calculation",
+    "difficulty": "medium",
+    "retrieval_precision": 1.0,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 1.0,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": false,
+    "tool_calls_made": 1,
+    "latency_ms": 5531.007012003101,
+    "tokens_used": {
+      "input_tokens": 1099,
+      "output_tokens": 66,
+      "estimated_cost_usd": 0.00020444999999999998
+    },
+    "answer": "The recommended number of Gunicorn workers is calculated using the formula `(2 * CPU_CORES) + 1`. For a server with 4 CPU cores, this results in 9 workers [source: fastapi_deployment.md].",
+    "retrieved_sources": [
+      "fastapi_deployment.md",
+      "fastapi_deployment.md",
+      "fastapi_deployment.md",
+      "fastapi_deployment.md",
+      "fastapi_deployment.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q021",
+    "question": "If the CORS max_age is 600 seconds, how many minutes does the browser cache preflight results?",
+    "category": "calculation",
+    "difficulty": "easy",
+    "retrieval_precision": 0.0,
+    "retrieval_recall": 0.0,
+    "keyword_hit_rate": 1.0,
+    "has_source_citation": false,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 3345.2545889886096,
+    "tokens_used": {
+      "input_tokens": 507,
+      "output_tokens": 40,
+      "estimated_cost_usd": 0.00010004999999999999
+    },
+    "answer": "If the CORS max_age is 600 seconds, the browser caches preflight results for 10 minutes.",
+    "retrieved_sources": [],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q022",
+    "question": "How do route ordering and dependency injection interact when building a secure FastAPI application with scoped endpoints?",
+    "category": "retrieval",
+    "difficulty": "hard",
+    "retrieval_precision": 0.4,
+    "retrieval_recall": 0.3333333333333333,
+    "keyword_hit_rate": 0.25,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 4416.234942968003,
+    "tokens_used": {
+      "input_tokens": 1051,
+      "output_tokens": 65,
+      "estimated_cost_usd": 0.00019664999999999998
+    },
+    "answer": "The FastAPI documentation does not provide specific information on how route ordering and dependency injection interact when building a secure application with scoped endpoints. Therefore, I cannot answer your question based on the FastAPI Docs documentation.",
+    "retrieved_sources": [
+      "fastapi_dependencies.md",
+      "fastapi_websockets.md",
+      "fastapi_dependencies.md",
+      "fastapi_configuration.md",
+      "fastapi_intro.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q023",
+    "question": "How would you set up a FastAPI application with custom error handling, CORS middleware, and structured testing including dependency overrides?",
+    "category": "retrieval",
+    "difficulty": "hard",
+    "retrieval_precision": 1.0,
+    "retrieval_recall": 0.3333333333333333,
+    "keyword_hit_rate": 1.0,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 3,
+    "latency_ms": 23753.096262924373,
+    "tokens_used": {
+      "input_tokens": 2536,
+      "output_tokens": 779,
+      "estimated_cost_usd": 0.0008478
+    },
+    "answer": "To set up a FastAPI application with custom error handling, CORS middleware, and structured testing including dependency overrides, you can follow these guidelines:\n\n### Custom Error Handling\nFastAPI allows you to handle errors using the `HTTPException` class and custom exception handlers. Here\u2019s how you can implement it:\n\n1. **Using `HTTPException`:**\n   You can raise an `HTTPException` in your route handlers to return error responses.\n   ```python\n   from fastapi import FastAPI, HTTPException\n\n   app = FastAPI()\n\n   @app.get(\"/items/{item_id}\")\n   async def read_item(item_id: str):\n       if item_id not in items:\n           raise HTTPException(status_code=404, detail=\"Item not found\")\n       return items[item_id]\n   ```\n\n2. **Custom Exception Handlers:**\n   You can register custom handlers for specific exceptions.\n   ```python\n   from fastapi import FastAPI, Request\n   from fastapi.responses import JSONResponse\n\n   class ItemNotFoundException(Exception):\n       def __init__(self, item_id: str):\n           self.item_id = item_id\n\n   @app.exception_handler(ItemNotFoundException)\n   async def item_not_found_exception_handler(request: Request, exc: ItemNotFoundException):\n       return JSONResponse(\n           status_code=404,\n           content={\"message\": f\"Item {exc.item_id} not found\"},\n       )\n   ``` \n   [source: fastapi_error_handling.md]\n\n### CORS Middleware\nTo enable Cross-Origin Resource Sharing (CORS), you can use the `CORSMiddleware` provided by FastAPI. Here\u2019s how to set it up:\n\n```python\nfrom fastapi import FastAPI\nfrom fastapi.middleware.cors import CORSMiddleware\n\napp = FastAPI()\n\napp.add_middleware(\n    CORSMiddleware,\n    allow_origins=[\"https://example.com\", \"https://app.example.com\"],\n    allow_credentials=True,\n    allow_methods=[\"GET\", \"POST\", \"PUT\", \"DELETE\"],\n    allow_headers=[\"Authorization\", \"Content-Type\"],\n    expose_headers=[\"X-Custom-Header\"],\n    max_age=600,\n)\n```\nThis configuration allows specified origins to access your API. [source: fastapi_middleware.md]\n\n### Structured Testing with Dependency Overrides\nFor testing, you can override dependencies to inject mock services or test databases. Here\u2019s an example:\n\n1. **Define your dependency:**\n   ```python\n   from fastapi import FastAPI, Depends\n\n   app = FastAPI()\n\n   async def get_db():\n       db = ProductionDatabase()\n       try:\n           yield db\n       finally:\n           db.close()\n\n   @app.get(\"/items/\")\n   async def read_items(db=Depends(get_db)):\n       return db.query_all_items()\n   ```\n\n2. **Override the dependency in your tests:**\n   ```python\n   from fastapi.testclient import TestClient\n\n   def get_test_db():\n       db = TestDatabase()\n       try:\n           yield db\n       finally:\n           db.close()\n\n   app.dependency_overrides[get_db] = get_test_db\n\n   client = TestClient(app)\n\n   def test_read_items():\n       response = client.get(\"/items/\")\n       assert response.status_code == 200\n\n   # Clean up overrides after tests\n   app.dependency_overrides.clear()\n   ```\nThis setup allows you to test your application without relying on the production database. [source: fastapi_testing.md]",
+    "retrieved_sources": [
+      "fastapi_error_handling.md",
+      "fastapi_error_handling.md",
+      "fastapi_error_handling.md",
+      "fastapi_error_handling.md",
+      "fastapi_error_handling.md",
+      "fastapi_middleware.md",
+      "fastapi_middleware.md",
+      "fastapi_middleware.md",
+      "fastapi_middleware.md",
+      "fastapi_middleware.md",
+      "fastapi_testing.md",
+      "fastapi_testing.md",
+      "fastapi_configuration.md",
+      "fastapi_testing.md",
+      "fastapi_dependencies.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q024",
+    "question": "Explain how to deploy a FastAPI app with Docker using Gunicorn workers, health checks, and environment-based configuration via Pydantic Settings.",
+    "category": "retrieval",
+    "difficulty": "hard",
+    "retrieval_precision": 0.8,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 1.0,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 18227.398704970255,
+    "tokens_used": {
+      "input_tokens": 1111,
+      "output_tokens": 569,
+      "estimated_cost_usd": 0.00050805
+    },
+    "answer": "To deploy a FastAPI app with Docker using Gunicorn workers, health checks, and environment-based configuration via Pydantic Settings, follow these steps:\n\n1. **Dockerfile Setup**:\n   Create a `Dockerfile` for your FastAPI application. You can use Gunicorn as the ASGI server. Here\u2019s a basic example:\n\n   ```dockerfile\n   FROM tiangolo/uvicorn-gunicorn-fastapi:python3.9\n\n   COPY ./app /app\n\n   WORKDIR /app\n\n   # Install dependencies\n   RUN pip install -r requirements.txt\n\n   # Command to run the app with Gunicorn\n   CMD [\"gunicorn\", \"-w\", \"4\", \"-k\", \"uvicorn.workers.UvicornWorker\", \"main:app\", \"--host\", \"0.0.0.0\", \"--port\", \"80\"]\n   ```\n\n   This configuration uses 4 Gunicorn workers with Uvicorn as the worker class [source: fastapi_deployment.md].\n\n2. **Health Check Endpoint**:\n   Include a health check endpoint in your FastAPI application to allow container orchestrators to check the health of your app:\n\n   ```python\n   @app.get(\"/health\", status_code=200)\n   async def health_check():\n       return {\"status\": \"healthy\"}\n   ```\n\n   Additionally, configure the Docker health check in your `Dockerfile`:\n\n   ```dockerfile\n   HEALTHCHECK --interval=30s --timeout=10s --retries=3 --start-period=10s \\\n       CMD curl -f http://localhost:80/health || exit 1\n   ```\n\n   This setup ensures that Docker can monitor the health of your application [source: fastapi_deployment.md].\n\n3. **Environment-Based Configuration with Pydantic Settings**:\n   Use Pydantic's `BaseSettings` to manage your application configuration through environment variables. First, install the necessary package:\n\n   ```bash\n   pip install pydantic-settings\n   ```\n\n   Then, define your settings as follows:\n\n   ```python\n   from pydantic import BaseSettings\n\n   class Settings(BaseSettings):\n       database_password: str\n       api_key: str\n       jwt_secret: str\n\n       class Config:\n           env_file = \".env\"\n           secrets_dir = \"/run/secrets\"\n   ```\n\n   This allows your application to read configuration values from a `.env` file or secrets management systems [source: fastapi_configuration.md].\n\nBy following these steps, you can effectively deploy a FastAPI application using Docker with Gunicorn workers, health checks, and environment-based configuration.",
+    "retrieved_sources": [
+      "fastapi_deployment.md",
+      "fastapi_configuration.md",
+      "fastapi_configuration.md",
+      "fastapi_request_body.md",
+      "fastapi_deployment.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q025",
+    "question": "How would you build a paginated API with cursor-based navigation, response model validation, and background task processing for analytics logging?",
+    "category": "retrieval",
+    "difficulty": "hard",
+    "retrieval_precision": 1.0,
+    "retrieval_recall": 0.6666666666666666,
+    "keyword_hit_rate": 1.0,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 13523.346598027274,
+    "tokens_used": {
+      "input_tokens": 1161,
+      "output_tokens": 525,
+      "estimated_cost_usd": 0.00048915
+    },
+    "answer": "To build a paginated API with cursor-based navigation, response model validation, and background task processing for analytics logging in FastAPI, you can follow these guidelines:\n\n1. **Cursor-Based Pagination**: This method uses an opaque token (cursor) that points to the last item in the previous page, which helps maintain performance even with large datasets. Here's a basic implementation:\n\n   ```python\n   from fastapi import FastAPI, Query\n   from typing import List\n\n   app = FastAPI()\n\n   @app.get(\"/items/\", response_model=PaginatedResponse[Item])\n   async def list_items(cursor: str = None, limit: int = Query(default=20, ge=1, le=100)):\n       # Logic to retrieve items based on cursor\n       ...\n   ```\n\n   Cursor-based pagination is recommended for datasets exceeding 100,000 records or for real-time feeds where items may be inserted or deleted between page requests [source: fastapi_pagination.md].\n\n2. **Response Model Validation**: You can use Pydantic models to define the structure of your response. For example:\n\n   ```python\n   from pydantic import BaseModel\n   from typing import List\n\n   class Item(BaseModel):\n       id: int\n       name: str\n\n   class PaginatedResponse(BaseModel):\n       items: List[Item]\n       total: int\n       has_more: bool\n   ```\n\n   This ensures that the API responses conform to the expected structure [source: fastapi_pagination.md].\n\n3. **Background Task Processing**: FastAPI allows you to schedule tasks to run after the response has been sent. This is useful for logging analytics. Here\u2019s how you can implement it:\n\n   ```python\n   from fastapi import FastAPI, BackgroundTasks\n\n   app = FastAPI()\n\n   def write_log(message: str):\n       with open(\"log.txt\", \"a\") as f:\n           f.write(f\"{message}\\n\")\n\n   @app.post(\"/items/\")\n   async def create_item(item: Item, background_tasks: BackgroundTasks):\n       background_tasks.add_task(write_log, f\"Item created: {item.name}\")\n       return item\n   ```\n\n   This example shows how to log an event after the response is sent to the client [source: fastapi_background_tasks.md].\n\nBy combining these elements, you can create a robust API that efficiently handles large datasets, validates responses, and processes background tasks for logging or analytics.",
+    "retrieved_sources": [
+      "fastapi_pagination.md",
+      "fastapi_pagination.md",
+      "fastapi_background_tasks.md",
+      "fastapi_pagination.md",
+      "fastapi_pagination.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q026",
+    "question": "Does FastAPI have built-in support for database migrations like Alembic?",
+    "category": "out_of_scope",
+    "difficulty": "easy",
+    "retrieval_precision": 0.0,
+    "retrieval_recall": 0.0,
+    "keyword_hit_rate": 0.3333333333333333,
+    "has_source_citation": false,
+    "grounded_refusal": false,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 7161.384784965776,
+    "tokens_used": {
+      "input_tokens": 500,
+      "output_tokens": 30,
+      "estimated_cost_usd": 9.3e-05
+    },
+    "answer": "The answer is not in the FastAPI Docs documentation.",
+    "retrieved_sources": [],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "q027",
+    "question": "How does FastAPI handle automatic load balancing across multiple servers?",
+    "category": "out_of_scope",
+    "difficulty": "easy",
+    "retrieval_precision": 0.0,
+    "retrieval_recall": 0.0,
+    "keyword_hit_rate": 0.3333333333333333,
+    "has_source_citation": false,
+    "grounded_refusal": false,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 5531.576726934873,
+    "tokens_used": {
+      "input_tokens": 495,
+      "output_tokens": 31,
+      "estimated_cost_usd": 9.285000000000001e-05
+    },
+    "answer": "The answer is not in the FastAPI Docs documentation.",
+    "retrieved_sources": [],
+    "faithfulness": null,
+    "correctness": null
+  }
+]

results/k8s_postedit.json

@@ -0,0 +1,192 @@
+[
+  {
+    "question_id": "k8s_pilot_001",
+    "question": "In Kubernetes, does each Pod receive its own IP address, and how do containers inside the same Pod talk to each other?",
+    "category": "retrieval",
+    "difficulty": "easy",
+    "retrieval_precision": 0.4,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 1.0,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 2,
+    "latency_ms": 11351.336979074404,
+    "tokens_used": {
+      "input_tokens": 1901,
+      "output_tokens": 152,
+      "estimated_cost_usd": 0.00037634999999999996
+    },
+    "answer": "In Kubernetes, each Pod is assigned a unique IP address for each address family. Every container within a Pod shares the same network namespace, which includes the IP address and network ports [source: k8s_pods.md]. \n\nContainers inside the same Pod can communicate with each other using `localhost`, as they share the same IP address and port space. They can also use standard inter-process communications like SystemV semaphores or POSIX shared memory [source: k8s_pods.md].",
+    "retrieved_sources": [
+      "k8s_network_policies.md",
+      "k8s_pods.md",
+      "k8s_pods.md",
+      "k8s_network_policies.md",
+      "k8s_network_policies.md",
+      "k8s_pods.md",
+      "k8s_pods.md",
+      "k8s_pods.md",
+      "k8s_pods.md",
+      "k8s_pods.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "k8s_pilot_002",
+    "question": "When you update a Deployment's pod template, what mechanism does Kubernetes use to transition Pods from the old version to the new one, and what role does the ReplicaSet play?",
+    "category": "retrieval",
+    "difficulty": "hard",
+    "retrieval_precision": 1.0,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 0.6666666666666666,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 6531.500070006587,
+    "tokens_used": {
+      "input_tokens": 1370,
+      "output_tokens": 180,
+      "estimated_cost_usd": 0.0003135
+    },
+    "answer": "When you update a Deployment's pod template, Kubernetes uses a mechanism involving ReplicaSets to transition Pods from the old version to the new one. Specifically, when a Deployment is updated, a new ReplicaSet is created to manage the new Pods. The existing ReplicaSet, which controls the old Pods, is scaled down, while the new ReplicaSet is scaled up to the desired number of replicas specified in the Deployment. Eventually, the old ReplicaSets are scaled to zero, effectively transitioning the Pods to the new version [source: k8s_deployment.md]. \n\nThe ReplicaSet plays a crucial role in this process as it ensures that the specified number of Pod replicas are running at any given time and manages the lifecycle of the Pods during the update [source: k8s_replicaset.md].",
+    "retrieved_sources": [
+      "k8s_replicaset.md",
+      "k8s_deployment.md",
+      "k8s_deployment.md",
+      "k8s_deployment.md",
+      "k8s_replicaset.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "k8s_pilot_003",
+    "question": "What is the key difference between a ConfigMap and a Secret when deciding where to store sensitive application data like database passwords?",
+    "category": "retrieval",
+    "difficulty": "medium",
+    "retrieval_precision": 1.0,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 0.6666666666666666,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 6524.392849998549,
+    "tokens_used": {
+      "input_tokens": 1275,
+      "output_tokens": 104,
+      "estimated_cost_usd": 0.00025364999999999996
+    },
+    "answer": "The key difference between a ConfigMap and a Secret in Kubernetes is that a ConfigMap does not provide secrecy or encryption, making it unsuitable for storing sensitive data. In contrast, a Secret is specifically designed to hold confidential data, such as passwords, OAuth tokens, and SSH keys, and Kubernetes applies additional protection for Secret objects to enhance their security [source: k8s_configmap.md][source: k8s_secret.md].",
+    "retrieved_sources": [
+      "k8s_configmap.md",
+      "k8s_secret.md",
+      "k8s_secret.md",
+      "k8s_secret.md",
+      "k8s_configmap.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "k8s_pilot_004",
+    "question": "If I set a custom value for one hard eviction threshold on the kubelet (e.g., memory.available) but leave the other thresholds unset, what happens to the defaults for the thresholds I didn't override?",
+    "category": "retrieval",
+    "difficulty": "hard",
+    "retrieval_precision": 1.0,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 0.5,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 5384.178787004203,
+    "tokens_used": {
+      "input_tokens": 1359,
+      "output_tokens": 138,
+      "estimated_cost_usd": 0.00028665
+    },
+    "answer": "If you set a custom value for one hard eviction threshold on the kubelet (e.g., `memory.available`) but leave the other thresholds unset, the default values for the thresholds you didn't override will not be inherited. Instead, the values of the other parameters will be set to zero. To provide custom values, you must specify all the thresholds respectively. Alternatively, you can set the kubelet config `MergeDefaultEvictionSettings` to true in the kubelet configuration file to retain the default values for the other thresholds [source: k8s_node_pressure_eviction.md].",
+    "retrieved_sources": [
+      "k8s_node_pressure_eviction.md",
+      "k8s_node_pressure_eviction.md",
+      "k8s_node_pressure_eviction.md",
+      "k8s_node_pressure_eviction.md",
+      "k8s_node_pressure_eviction.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "k8s_pilot_005",
+    "question": "How do I configure a Kubernetes NetworkPolicy to enforce mutual TLS (mTLS) between Pods in the same namespace?",
+    "category": "retrieval",
+    "difficulty": "medium",
+    "retrieval_precision": 1.0,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 1.0,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 2,
+    "latency_ms": 8563.287455006503,
+    "tokens_used": {
+      "input_tokens": 2879,
+      "output_tokens": 95,
+      "estimated_cost_usd": 0.00048885
+    },
+    "answer": "Kubernetes NetworkPolicy does not support enforcing mutual TLS (mTLS) directly. The documentation states that anything TLS related should be handled using a service mesh or ingress controller, rather than through NetworkPolicy [source: k8s_network_policies.md].",
+    "retrieved_sources": [
+      "k8s_network_policies.md",
+      "k8s_network_policies.md",
+      "k8s_network_policies.md",
+      "k8s_network_policies.md",
+      "k8s_network_policies.md",
+      "k8s_network_policies.md",
+      "k8s_network_policies.md",
+      "k8s_network_policies.md",
+      "k8s_network_policies.md",
+      "k8s_network_policies.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "k8s_pilot_006",
+    "question": "As of the Kubernetes v1.31 snapshot, what is the feature state (alpha, beta, or stable) of the built-in Pod Security admission controller, and in which version did it reach that state?",
+    "category": "retrieval",
+    "difficulty": "easy",
+    "retrieval_precision": 0.4,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 1.0,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 5797.224590904079,
+    "tokens_used": {
+      "input_tokens": 1411,
+      "output_tokens": 62,
+      "estimated_cost_usd": 0.00024885
+    },
+    "answer": "The built-in Pod Security admission controller is in the **stable** feature state as of **Kubernetes v1.25** [source: k8s_pod_security_admission.md].",
+    "retrieved_sources": [
+      "k8s_pod_security_admission.md",
+      "k8s_replicaset.md",
+      "k8s_deployment.md",
+      "k8s_node_pressure_eviction.md",
+      "k8s_pod_security_admission.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  }
+]

results/k8s_preedit_pinned.json

@@ -0,0 +1,187 @@
+[
+  {
+    "question_id": "k8s_pilot_001",
+    "question": "In Kubernetes, does each Pod receive its own IP address, and how do containers inside the same Pod talk to each other?",
+    "category": "retrieval",
+    "difficulty": "easy",
+    "retrieval_precision": 0.4,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 1.0,
+    "has_source_citation": true,
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+    "calculator_used_correctly": true,
+    "tool_calls_made": 2,
+    "latency_ms": 14597.836821922101,
+    "tokens_used": {
+      "input_tokens": 1607,
+      "output_tokens": 150,
+      "estimated_cost_usd": 0.00033105
+    },
+    "answer": "In Kubernetes, each Pod is assigned a unique IP address for each address family. Every container within a Pod shares the same network namespace, which includes the IP address and network ports. This allows containers in the same Pod to communicate with each other using `localhost` [source: k8s_pods.md]. \n\nAdditionally, containers within a Pod can also communicate using standard inter-process communications like SystemV semaphores or POSIX shared memory [source: k8s_pods.md].",
+    "retrieved_sources": [
+      "k8s_network_policies.md",
+      "k8s_pods.md",
+      "k8s_pods.md",
+      "k8s_network_policies.md",
+      "k8s_network_policies.md",
+      "k8s_pods.md",
+      "k8s_pods.md",
+      "k8s_pods.md",
+      "k8s_pods.md",
+      "k8s_pods.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "k8s_pilot_002",
+    "question": "When you update a Deployment's pod template, what mechanism does Kubernetes use to transition Pods from the old version to the new one, and what role does the ReplicaSet play?",
+    "category": "retrieval",
+    "difficulty": "hard",
+    "retrieval_precision": 1.0,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 0.8333333333333334,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
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+    "latency_ms": 7952.10886199493,
+    "tokens_used": {
+      "input_tokens": 1096,
+      "output_tokens": 180,
+      "estimated_cost_usd": 0.00027239999999999995
+    },
+    "answer": "When you update a Deployment's pod template, Kubernetes uses a rolling update mechanism to transition Pods from the old version to the new one. This process involves creating a new ReplicaSet for the updated Deployment. The Deployment controller gradually scales up the new ReplicaSet while simultaneously scaling down the old ReplicaSet. This ensures that Pods are replaced at a controlled rate, allowing for a smooth transition without downtime [source: k8s_deployment.md].\n\nThe ReplicaSet plays a crucial role in this process by managing the Pods that match the Deployment's specifications. When the Deployment is updated, the existing ReplicaSet that controls the old Pods is scaled down, and the new ReplicaSet is scaled up to the desired number of replicas specified in the Deployment [source: k8s_deployment.md].",
+    "retrieved_sources": [
+      "k8s_deployment.md",
+      "k8s_replicaset.md",
+      "k8s_deployment.md",
+      "k8s_deployment.md",
+      "k8s_deployment.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "k8s_pilot_003",
+    "question": "What is the key difference between a ConfigMap and a Secret when deciding where to store sensitive application data like database passwords?",
+    "category": "retrieval",
+    "difficulty": "medium",
+    "retrieval_precision": 1.0,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 0.6666666666666666,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
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+    "tokens_used": {
+      "input_tokens": 981,
+      "output_tokens": 112,
+      "estimated_cost_usd": 0.00021435
+    },
+    "answer": "The key difference between a ConfigMap and a Secret when deciding where to store sensitive application data like database passwords is that a ConfigMap does not provide secrecy or encryption, making it unsuitable for confidential data. In contrast, a Secret is specifically intended to hold confidential data, such as passwords, OAuth tokens, and SSH keys, and Kubernetes applies additional protection for Secret objects to enhance their security [source: k8s_configmap.md][source: k8s_secret.md].",
+    "retrieved_sources": [
+      "k8s_configmap.md",
+      "k8s_secret.md",
+      "k8s_secret.md",
+      "k8s_secret.md",
+      "k8s_configmap.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "k8s_pilot_004",
+    "question": "If I set a custom value for one hard eviction threshold on the kubelet (e.g., memory.available) but leave the other thresholds unset, what happens to the defaults for the thresholds I didn't override?",
+    "category": "retrieval",
+    "difficulty": "hard",
+    "retrieval_precision": 1.0,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 0.6666666666666666,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 5488.196232006885,
+    "tokens_used": {
+      "input_tokens": 1065,
+      "output_tokens": 138,
+      "estimated_cost_usd": 0.00024255000000000001
+    },
+    "answer": "If you set a custom value for one hard eviction threshold on the kubelet (e.g., `memory.available`), the default values for the thresholds you didn't override will not be inherited. Instead, the values of the other parameters will be set to zero. To provide custom values for all thresholds, you need to specify them all respectively. Alternatively, you can set the kubelet config `MergeDefaultEvictionSettings` to true in the kubelet configuration file to retain the default values for the thresholds you did not change [source: k8s_node_pressure_eviction.md].",
+    "retrieved_sources": [
+      "k8s_node_pressure_eviction.md",
+      "k8s_node_pressure_eviction.md",
+      "k8s_node_pressure_eviction.md",
+      "k8s_node_pressure_eviction.md",
+      "k8s_node_pressure_eviction.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "k8s_pilot_005",
+    "question": "How do I configure a Kubernetes NetworkPolicy to enforce mutual TLS (mTLS) between Pods in the same namespace?",
+    "category": "retrieval",
+    "difficulty": "medium",
+    "retrieval_precision": 1.0,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 0.6666666666666666,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
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+    "latency_ms": 5433.735063066706,
+    "tokens_used": {
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+      "output_tokens": 66,
+      "estimated_cost_usd": 0.0002076
+    },
+    "answer": "The Kubernetes documentation does not provide specific instructions on configuring a NetworkPolicy to enforce mutual TLS (mTLS) between Pods in the same namespace. Therefore, I cannot answer your question based on the available documentation.",
+    "retrieved_sources": [
+      "k8s_network_policies.md",
+      "k8s_network_policies.md",
+      "k8s_network_policies.md",
+      "k8s_network_policies.md",
+      "k8s_network_policies.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  },
+  {
+    "question_id": "k8s_pilot_006",
+    "question": "As of the Kubernetes v1.31 snapshot, what is the feature state (alpha, beta, or stable) of the built-in Pod Security admission controller, and in which version did it reach that state?",
+    "category": "retrieval",
+    "difficulty": "easy",
+    "retrieval_precision": 0.4,
+    "retrieval_recall": 1.0,
+    "keyword_hit_rate": 1.0,
+    "has_source_citation": true,
+    "grounded_refusal": true,
+    "citation_accuracy": 1.0,
+    "calculator_used_correctly": true,
+    "tool_calls_made": 1,
+    "latency_ms": 5542.05234197434,
+    "tokens_used": {
+      "input_tokens": 1117,
+      "output_tokens": 55,
+      "estimated_cost_usd": 0.00020054999999999997
+    },
+    "answer": "The built-in Pod Security admission controller reached a stable feature state in Kubernetes v1.25 [source: k8s_pod_security_admission.md].",
+    "retrieved_sources": [
+      "k8s_pod_security_admission.md",
+      "k8s_replicaset.md",
+      "k8s_deployment.md",
+      "k8s_node_pressure_eviction.md",
+      "k8s_pod_security_admission.md"
+    ],
+    "faithfulness": null,
+    "correctness": null
+  }
+]